



'''simple docstring'''



# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
							import torch
class 						lowerCamelCase_				(       TensorFormatter[Mapping, 'torch.Tensor', Mapping]      ):







						def __init__(	self :   Optional[Any]					,     lowerCAmelCase__ :   str=None					,     **lowerCAmelCase__ :   List[Any]					):

													"""simple docstring"""
													super().__init__(features=lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   Tuple         =      torch_tensor_kwargs
													import torch  # noqa import torch at initialization







						def        __lowercase     (	self :   Optional[Any]					,     lowerCAmelCase__ :   Dict					):

													"""simple docstring"""
													import torch

													if isinstance(lowerCAmelCase__					,     lowerCAmelCase__					) and column:
																				if all(
																				    isinstance(lowerCAmelCase__					,     torch.Tensor					) and x.shape == column[0].shape and x.dtype == column[0].dtype
																				    for x in column					):
																											return torch.stack(lowerCAmelCase__					)
													return column







						def        __lowercase     (	self :   Dict					,     lowerCAmelCase__ :   List[str]					):

													"""simple docstring"""
													import torch

													if isinstance(lowerCAmelCase__					,     (str, bytes, type(lowerCAmelCase__					))					):
																				return value
													elif isinstance(lowerCAmelCase__					,     (np.character, np.ndarray)					) and np.issubdtype(value.dtype					,     np.character					):
																				return value.tolist()

													SCREAMING_SNAKE_CASE       :   Any         =      {}

													if isinstance(lowerCAmelCase__					,     (np.number, np.ndarray)					) and np.issubdtype(value.dtype					,     np.integer					):
																				SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      {'''dtype''': torch.intaa}
													elif isinstance(lowerCAmelCase__					,     (np.number, np.ndarray)					) and np.issubdtype(value.dtype					,     np.floating					):
																				SCREAMING_SNAKE_CASE       :   int         =      {'''dtype''': torch.floataa}
													elif config.PIL_AVAILABLE and "PIL" in sys.modules:
																				import PIL.Image

																				if isinstance(lowerCAmelCase__					,     PIL.Image.Image					):
																											SCREAMING_SNAKE_CASE       :   List[str]         =      np.asarray(lowerCAmelCase__					)
													return torch.tensor(lowerCAmelCase__					,     **{**default_dtype, **self.torch_tensor_kwargs}					)







						def        __lowercase     (	self :   Tuple					,     lowerCAmelCase__ :   Tuple					):

													"""simple docstring"""
													import torch

													# support for torch, tf, jax etc.
													if hasattr(lowerCAmelCase__					,     '''__array__'''					) and not isinstance(lowerCAmelCase__					,     torch.Tensor					):
																				SCREAMING_SNAKE_CASE       :   List[str]         =      data_struct.__array__()
													# support for nested types like struct of list of struct
													if isinstance(lowerCAmelCase__					,     np.ndarray					):
																				if data_struct.dtype == object:  # torch tensors cannot be instantied from an array of objects
																											return self._consolidate([self.recursive_tensorize(lowerCAmelCase__					) for substruct in data_struct]					)
													elif isinstance(lowerCAmelCase__					,     (list, tuple)					):
																				return self._consolidate([self.recursive_tensorize(lowerCAmelCase__					) for substruct in data_struct]					)
													return self._tensorize(lowerCAmelCase__					)







						def        __lowercase     (	self :   str					,     lowerCAmelCase__ :   dict					):

													"""simple docstring"""
													return map_nested(self._recursive_tensorize					,     lowerCAmelCase__					,     map_list=lowerCAmelCase__					)







						def        __lowercase     (	self :   Dict					,     lowerCAmelCase__ :   pa.Table					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   str         =      self.numpy_arrow_extractor().extract_row(lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      self.python_features_decoder.decode_row(lowerCAmelCase__					)
													return self.recursive_tensorize(lowerCAmelCase__					)







						def        __lowercase     (	self :   Any					,     lowerCAmelCase__ :   pa.Table					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   int         =      self.numpy_arrow_extractor().extract_column(lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      self.python_features_decoder.decode_column(lowerCAmelCase__					,     pa_table.column_names[0]					)
													SCREAMING_SNAKE_CASE       :   Any         =      self.recursive_tensorize(lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   List[str]         =      self._consolidate(lowerCAmelCase__					)
													return column







						def        __lowercase     (	self :   List[Any]					,     lowerCAmelCase__ :   pa.Table					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      self.numpy_arrow_extractor().extract_batch(lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   Dict         =      self.python_features_decoder.decode_batch(lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      self.recursive_tensorize(lowerCAmelCase__					)
													for column_name in batch:
																				SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      self._consolidate(batch[column_name]					)
													return batch









'''simple docstring'''



import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase_  :							List[Any]             =    logging.get_logger(__name__)
def        UpperCAmelCase (    A	:					Dict					):
							SCREAMING_SNAKE_CASE       :   List[str]         =      torch.load(A       ,		map_location='''cpu'''					)
							if "model" in sd.keys():
														SCREAMING_SNAKE_CASE       :   List[Any]         =      torch.load(A       ,		map_location='''cpu'''					)['''model''']

							# pop unnecessary weights
							SCREAMING_SNAKE_CASE       :   Optional[int]         =      [
							    '''decoder.version''',
							    '''decoder.output_projection.weight''',
							]
							for key in keys_to_delete:
														if key in sd:
																					sd.pop(A					)

							SCREAMING_SNAKE_CASE       :   int         =      {
							    '''decoder.project_in_dim.weight''': '''decoder.project_in.weight''',
							    '''decoder.project_out_dim.weight''': '''decoder.project_out.weight''',
							    '''decoder.layer_norm.weight''': '''decoder.final_layer_norm.weight''',
							    '''decoder.layer_norm.bias''': '''decoder.final_layer_norm.bias''',
							}
							for old_key, new_key in keys_to_rename.items():
														if old_key in sd:
																					SCREAMING_SNAKE_CASE       :   Optional[int]         =      sd.pop(A					)

							SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      list(sd.keys()					)
							for key in keys:
														if ".qkv_proj." in key:
																					SCREAMING_SNAKE_CASE       :   str         =      sd[key]
																					# We split QKV in separate Q,K,V

																					SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      key.replace('''.qkv_proj.'''       ,		'''.q_proj.'''					)
																					SCREAMING_SNAKE_CASE       :   str         =      key.replace('''.qkv_proj.'''       ,		'''.k_proj.'''					)
																					SCREAMING_SNAKE_CASE       :   Any         =      key.replace('''.qkv_proj.'''       ,		'''.v_proj.'''					)

																					SCREAMING_SNAKE_CASE       :   Tuple         =      value.shape[0]
																					assert depth % 3 == 0
																					# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
																					# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
																					SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   Tuple         =      torch.split(A       ,		depth // 3       ,		dim=0					)

																					SCREAMING_SNAKE_CASE       :   List[str]         =      q
																					SCREAMING_SNAKE_CASE       :   List[str]         =      k
																					SCREAMING_SNAKE_CASE       :   List[Any]         =      v
																					del sd[key]

							return sd
@torch.no_grad()
def        UpperCAmelCase (    A	:					int       ,		A	:					Union[str, Any]       ,		A	:					Dict=None					):
							SCREAMING_SNAKE_CASE       :   Optional[Any]         =      load_checkpoint(A					)

							if config is not None:
														SCREAMING_SNAKE_CASE       :   Tuple         =      OPTConfig.from_pretrained(A					)
							else:
														SCREAMING_SNAKE_CASE       :   int         =      OPTConfig()

							SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      OPTModel(A					).half().eval()
							model.load_state_dict(A					)

							# Check results
							Path(A					).mkdir(exist_ok=A					)
							model.save_pretrained(A					)


if __name__ == "__main__":
							lowerCAmelCase_  :							List[str]             =    argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--fairseq_path',
							    type=str,
							    help=(
							        'path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:'
							        ' https://huggingface.co/models?other=opt_metasq'
							    ),
							)
							parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
							parser.add_argument('--hf_config', default=None, type=str, help='Define HF config.')
							lowerCAmelCase_  :							str             =    parser.parse_args()
							convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)




